Experimenting multiple approaches for teaching meta-modeling
>>Teaching with software tools <<
Presented at https://www.jamk.fi/en/Event-Calendar/the-global-faculty-colloquium/global-faculty-research-colloquium/
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--
Slides of my presentation for Habilitation (HDR) defense in computer science (Informatique section 27 CNU) at University Paris 1 Panthéon Sorbonne on Friday January 2017.
Video recording is visible on https://www.youtube.com/watch?v=1ro_iaI-roA
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Contributions to the multidisciplinarity of computer science and ISSaïd Assar
Les diapos de ma présentation HDR en informatique (CNU section 27) à l'université Paris 1 Panthéon Sorbonne le vendredi 20 janvier 2017. L'enregistrement vidéo de la soutenance est visible sur https://www.youtube.com/watch?v=1ro_iaI-roA
--
Slides of my presentation for Habilitation (HDR) defense in computer science (Informatique section 27 CNU) at University Paris 1 Panthéon Sorbonne on Friday January 2017.
Video recording is visible on https://www.youtube.com/watch?v=1ro_iaI-roA
Computer science curriculum based on Program learning outcomes and objectivesJawad Khan
Designing curriculum of computer sciences and computer engineering on the bases of needs in the industry. Program learning objectives have been dealt in a case study in this presentation
Software Engineering Patterns for Machine Learning ApplicationsHironori Washizaki
Hironori Washizaki, Software Engineering Patterns for Machine Learning Applications, 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI 2021), Keynote, August 28, Online, 2021.
Studying Software Engineering Patterns for Designing Machine Learning SystemsHironori Washizaki
Hironori Washizaki, Hiromu Uchida, Foutse Khomh and Yann-Gaël Guéhéneuc, “Studying Software Engineering Patterns for Designing Machine Learning Systems,” The 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019), Tokyo, Japan, on December 13-14, 2019.
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In the context of the Internet of Things (IoT), intelligent systems (IS) are increasingly relying on Machine Learning (ML) techniques. Given the opaqueness of most ML techniques, however, humans have to rely on their intuition to fully understand the IS outcomes: helping them is the target of eXplainable Artificial Intelligence (XAI). Current solutions – mostly too specific, and simply aimed at making ML easier to interpret – cannot satisfy the needs of IoT, characterised by heterogeneous stimuli, devices, and data-types concurring in the composition of complex information structures. Moreover, Multi-Agent Systems (MAS) achievements and advancements are most often ignored, even when they could bring about key features like explainability and trustworthiness. Accordingly, in this paper we (i) elicit and discuss the most significant issues affecting modern IS, and (ii) devise the main elements and related interconnections paving the way towards reconciling interpretable and explainable IS using MAS.
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Presentazione of: The role of Artificial Intelligence in architectural design...Giuseppe Gallo
The proliferation of data together with the increase of computing power has triggered a new interest in artificial intelligence. Machine learning and Deep Learning techniques are omnipresent in contemporary discourse, generating new enthusiasms and fears in our society. The world of architecture has not remained external to this phenomenon. If on the one hand, these techniques promise great results, on the other we are still in an exploratory phase. It is then necessary, in our opinion, to understand what the roles of this technology could be within the architectural design process, and with which scopes they can facilitate such a complex profession as that of the architect. On this subject we made ten interviews with as many designers and researchers in the AEC industry, In the article we will report a summary of their testimonies, comparing and commenting on the responses of the designers, with the aim of understanding the potentials of using artificial intelligence methods within the design process, report their perceptions on how artificial intelligence techniques can affect the architect's approach to the project, concluding with some reflections on the critical issues identified during the interviews with the designers.
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Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals.
This is the presentation of the paper "Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data" which I delivered at the Learning Analytics and Knowledge conference 2017 in Vancouver, Canada.
http://dl.acm.org/citation.cfm?id=3027447&CFID=912205331&CFTOKEN=43442860
Towards XMAS: eXplainability through Multi-Agent SystemsGiovanni Ciatto
In the context of the Internet of Things (IoT), intelligent systems (IS) are increasingly relying on Machine Learning (ML) techniques. Given the opaqueness of most ML techniques, however, humans have to rely on their intuition to fully understand the IS outcomes: helping them is the target of eXplainable Artificial Intelligence (XAI). Current solutions – mostly too specific, and simply aimed at making ML easier to interpret – cannot satisfy the needs of IoT, characterised by heterogeneous stimuli, devices, and data-types concurring in the composition of complex information structures. Moreover, Multi-Agent Systems (MAS) achievements and advancements are most often ignored, even when they could bring about key features like explainability and trustworthiness. Accordingly, in this paper we (i) elicit and discuss the most significant issues affecting modern IS, and (ii) devise the main elements and related interconnections paving the way towards reconciling interpretable and explainable IS using MAS.
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Experimenting multiple approaches for teaching meta-modeling
1. Experimenting multiple approaches
for teaching meta-modeling
>>Teaching with software tools <<
The Global Faculty Research Colloquium, 2nd edition
Saïd Assar, Ph.D, HDR
Associate professor in Information Systems
Institut Mines-Télécom Business School
(ex- Telecom École de Management)
2. About my institution
2
Telecom Sud Paris
Institut Mines-Télécom: 13 leading schools in industrial and
ICT engineering and in management
Telecom Paristech
IMT Atlantique
Institut Mines Telecom
Business School
3. Working with the university
3
Since 1999, long-term research collaboration with CRI at
Paris 1 Sorbonne
5. About the importance of pedagogy …
• Teaching mathematics …
• The impact of a teacher
5
6. I. Introduction
• Frequent pedagogical issues
– Linking theory to practice
– Making theory concrete
– Showing the utility of theory
6
“Theory” as
conceptual, abstract
knowledge
7. I. Introduction (cont’d)
• Teaching context
– Information Systems engineering
– Design methods
– Data modeling
– Process modeling
– Meta-modeling
7
8. II. About modeling in design 8
Models are essential artifacts in engineering
To communicate
To plan and organize
To understand, correct, maintain and evolve
They can describe existing or future objects and situations
Models to analyze existing artifacts
Models to design and build artifacts
Required competences for
management students :
reading and understanding a
model
9. II. About modeling in design 9
• Specificity of models in IS …
Conventional design
IT artifact design
10. III. Meta-modeling
• Meta-models are “generic” models, i.e.,
category of models
• Simple example : a trans-national car
tracking system
– A specific “car plate model” for each country
– All car plate models share certain characteristics
• How to represent the knowledge about these
set of data?
10
1
11. III. Meta-modeling 11
• Another example : family of development
methods
– A specific “development method” for each
category of projects
– All development models share certain
characteristics
• How to represent the knowledge about
these set of data?
Method M1 for simple projects
S1
S2
S3
SN
…
Method M2 for
large projects
L1
L2
L3
LM
…
Agile method M3 for
risky projects
G1
G2
G3
GK
…
12. III. Meta-modeling
• Issues in teaching meta-modeling
– Abstract concept
– Difficult to operationalize
– Recurring questions
• How can a meta-model be also a model?
• What is a meta-model for?
• Software tools based approach
– To illustrate multiple levels of instantiation
– To show concrete applications
12
13. IV. Tools for teaching meta-modeling
• ConceptBase
– A deductive database supporting object-centered TELOS
modeling language
– Result from research project DAIDA (J. Mylopoulos, M. Jarke
et al. - 1990')
– Targeted towards Model Engineering
– Continued as an open source project (M. A. Jeusfled)
13
14. IV. Tools for teaching meta-modeling 14
• Multiple levels of instantiation with CB
Excellent feedback
Instantiation made concrete
… what for ?
15. IV. Tools for teaching meta-modeling
1
• MetaEdit
A software tool for met-model creation and tool customisation
Result of research project at Jyväskylä (K. Lyytinen, M. Rossi, S. Kelly et al. - 1990')
Actually commercialized by MetaCASE
Targeted towards Domain Specific Modeling
16. IV. Tools for teaching meta-modeling
1
• Model definition with MetaEdit
Meta-model definition
Generate
(Executable)
code
Domain Specific Model
creation
17. IV. Tools for teaching meta-modeling
10:39
Creation of a customized editor with MetaEdit
1. Meta-model definition
18. IV. Tools for teaching meta-modeling
10:39
Creation of a customized editor with MetaEdit
2. Domain Specific Model creation with customized editor
19. IV. Tools for teaching meta-modeling
10:39
Creation of a customized editor with MetaEdit
2. Domain Specific Model creation with customized editor
20. V. Conclusion
• Software tools based teaching
• Can be difficult to implement
– Necessity to learn the tool first …
– Learning the tool vs. learning the concepts …
– Software maintenance and evolution issues …
• Research oriented open source tools (e.g.
ConceptBase) versus Business oriented tools
(e.g. MetaCase, SAP, Microsoft, Oracle, etc.)
20
21. THANK YOU FOR YOUR ATTENTION !Any comments?
Homepage
http://fr.linkedin.com/pub/sa%C3%AFd-assar/4/68a/66a
http://fr.slideshare.net/SaidAssar/
https://twitter.com/Said_Assar/
http://www-public.tem-tsp.eu/~assar/
Editor's Notes
Les modèles servent plusieurs objectifs: quel modèle de l’immeuble va-t-on présenter au client?
Importance de l’usage des modèle « as is » et « to be »
Commenter sur le titre du cours Analysis et Design